Study on Bidding Strategies using Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{ChuanYue:thesis,
  author =       "Chuan Yue",
  title =        "Study on Bidding Strategies using Genetic Network
                 Programming",
  school =       "Waseda University",
  year =         "2012",
  address =      "Japan",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming",
  URL =          "http://jairo.nii.ac.jp/0069/00023944/en",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/1/Honbun-6057.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/2/Shinsa-6057.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40075/3/Gaiyo-6057.pdf",
  size =         "140 pages",
  abstract =     "Due to the explosive development of global network
                 structure, electronic commerce is increasingly playing
                 an important role in many organisations and individual
                 consumer's daily life. It offers opportunities to
                 significantly improve the way for businesses
                 interactions between both customers and suppliers. More
                 and more large scale and decentralised e-commerce
                 mechanisms have emerged in industrial and commercial
                 domains in a wide range.

                 In particular, among all these applications, online
                 auctions, which are flexible pricing mechanisms over
                 Internet, make the physical limitations of traditional
                 auctions disappear. They gain their extra popularity in
                 the daily life and attract globally dispersed users due
                 to having the characteristics that bargaining and
                 negotiation besides all of the convenience. Thus, on
                 line auctions become one of the most widely studied and
                 employed negotiation mechanisms today. Traditionally,
                 in most current online auction applications, the
                 traders are generally humans who operate all the
                 behaviours to make transactions. These behaviours may
                 involve observing the auctions, analysing the auction
                 information, and bidding the suitable price for the
                 items. However, facing the increasingly demanding
                 requirements and complexity of online trading, this
                 kind of manual operation does not reveal the full
                 potential of this new mode of commerce. Thus, in order
                 to relieve the users and be more effective, exploring
                 possible types and automating the behaviours in the
                 online auction attract high interest.

                 Now, in many studies, the agent-oriented auction
                 mechanism, with its emphasis on autonomous actions and
                 flexible interactions, arises as an effective and
                 robust model for the dynamic and sensitive commerce
                 environment. In such systems, the agent acts flexibly
                 on behalf of its owner and is capable of local
                 decision-making based on the environment information
                 and pre-knowledge about the system.

                 Among many different types of online auction, two of
                 the most popular and studied types are Multiple Round
                 English Auctions (MREA), which is single side auction,
                 and Continuous Double Auction (CDA), which is double
                 side auction. These auctions are newly emerged in
                 e-commerce era based on the traditional auction types.
                 They allow multiple agents to participate and one agent
                 can deal with several auctions continuously or
                 simultaneously, which are effective auction types to
                 save time and relieve the users. Towards to these
                 types, because there is no centralised system-wide
                 control, the major challenge for automatic bidding
                 strategies is to improve the degree of automation and
                 optimise the agent's bidding behaviour in order to
                 maximise the owner's profit. Most of the related
                 researches have been conducted by using heuristic
                 methods and fixed mathematical functions to compute the
                 final optimal bidding price for the items or to compute
                 how much should bid at each time step. Nevertheless,
                 because auction environments are complicated and highly
                 dynamic due to have many factors affecting each other,
                 these approaches are not flexible enough for the
                 dynamic environment, and there is no dominant
                 strategy.",
  abstract =     "Against this background, this thesis is concerned with
                 developing the intelligence of autonomous agent's
                 bidding strategy in order to make the agent to be more
                 efficient and competitive for agent-based online
                 auction mechanisms, especially in MREA and CDA. In
                 order to be more flexible and better exploit the market
                 information, Genetic Network Programming (GNP) is
                 firstly employed to the agent's bidding strategy since
                 its applicability and efficiency have been clarified in
                 complex and dynamic problems in many other fields. GNP
                 is one of the evolutionary optimization techniques
                 developed as an extension of Genetic Algorithm (GA) and
                 Genetic Programming (GP), which uses compact directed
                 graph structures as solutions. Basically speaking, in
                 the proposed method, the GNP population represents the
                 group of potential bidding strategies, and each
                 individual uses the as-if/then decision-making
                 functions to judge the auction information and guides
                 the agent to take the suitable actions under different
                 situations. Thus, it could be flexible and capable to
                 adaptive to various auction situations. During the
                 evolution, the GNP structure will be systematically
                 organized, and finally, the individual which can obtain
                 the highest profit is selected as the optimal bidding
                 strategy at the end of training phase.

                 In chapter 2, we introduced the conception of MREA and
                 CDA in detail, which are the study environments in this
                 thesis. The related researches are also introduced.

                 In chapter 3, focusing on MREA, the bidding strategy
                 for the auction agents in MREA is proposed using GNP.
                 The performance of GNP-based agents is evaluated and
                 studied in two situations: MREA is no time limit (NTL),
                 and MREA is time limit (TL). Furthermore, according to
                 the amount of the money each agent has, each situation
                 is divided into 2 cases: general case and poorest case.
                 All the participating agents in the simulations use GNP
                 strategy. This chapter aims to study and analyse the
                 capability and effectiveness of GNP for guiding bidding
                 actions through the phenomenon of the simulations. The
                 simulation results reveal that the agents using GNP
                 strategy can understand various environments well
                 through experiences and become smarter through
                 evolution.

                 In chapter 4, as an extension of the bidding strategy
                 in chapter 3, in order to improving the agent's
                 intelligence and sensitivity, an enhanced bidding
                 strategy for MREA is developed using GNP. Firstly, the
                 GNP structure is modified to be able to judge more
                 kinds of information and more situations at a time.
                 Secondly, the strategy is improved to be able to
                 consider the bidder's attitude towards to each good,
                 which makes the strategy to be more personalised for
                 each bidder and could make the bidder more satisfied
                 with the auction result and profit. The proposed
                 strategy is compared with the previous GNP strategy and
                 the other conventional strategies in the simulations.
                 The simulation results demonstrated that the proposed
                 method can outperform the previous one and is more
                 competitive than the agents based on mathematical
                 functions.

                 In chapter 5, focusing on CDA, GNP with rectify nodes
                 (GNP-RN) has been applied for CDA bidding strategy
                 combined with proposed heuristic rules, which are
                 derived based on the common believes for assisting
                 agent's bidding behaviour. GNP-RN is developed aiming
                 to guide the agent to be competitive under different
                 CDA environments, and maximise the agent's profit
                 without losing chances for trading. Rectify Node (RN)
                 is a newly proposed kind of nodes, which is used for
                 bringing more flexible and various options for bidding
                 action choices. 4 groups of simulations are designed to
                 compare GNP-RN with conventional GNP and other
                 strategies in CDA. In each simulation, the kinds of
                 opponent agents are different in order to fully analyse
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